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About This Role
- Build and scale production-grade data infrastructure for agentic AI systems that execute complex, multi-step work with autonomy, state, memory, and tool use.
- You will engineer resilient platforms for long-running agent workflows, multi-agent coordination, and adaptive execution in enterprise environments - while delivering the data pipelines and integrations that connect agents to enterprise data, legacy systems, and simulation tools.
- This role emphasizes reliability, control, observability, data quality, and governance for agentic AI systems over conversational chatbot patterns.
- Key Responsibilities Agentic Orchestration • Productionize graph-based orchestration for planner-executor-validator, orchestrator-worker, and similar patterns. • Implement explicit state and control flows: branching, loops, routing, interruption points, and human approval checkpoints. • Enable robust agent-tool integration across APIs, services, data systems, and enterprise platforms. • Support multi-agent collaboration patterns with guardrails for coordination, delegation, and convergence.
- Data Engineering and Integration • Design and maintain data pipelines connecting distributed enterprise data to a centralized semantic/knowledge layer that ensures clean, unified inputs for agent consumption. • Build and operate event streaming, API management, and systems integration infrastructure to enable trustable, consistent data for agentic workflows. • Build and maintain a data catalog and onboarding guides for teams adopting the agentic platform.
- DevOps and Reliability for AI Agent Systems • Define and track SLIs/SLOs for task completion reliability, reasoning quality, tool-call success, latency, and cost across agent pipelines. • Implement CI/CD practices tailored for agent deployments - versioning agent configurations, prompts, tools, and orchestration logic as code. • Build incident response and reliability practices for autonomous workflows, including safe rollback, pause/resume, and controlled retries. • Optimize compute, storage, and inference paths for sustained agent throughput and cost efficiency.
- Observability, Evaluation, and Control • Implement full-stack observability for agent runs - traces, state transitions, tool telemetry, data quality signals, outcomes, and replay ability. • Build continuous evaluation pipelines for agent behavior, including correctness, safety, drift, and regression detection. • Provide actionable operational dashboards for quality, reliability, data health, and cost in production agent systems.
Requirements
- are required to be initially considered for this position.
- Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates.
- Minimum Qualifications Master’s degree in software engineering, Computer Engineering, Information Technology, or related field with 5+ years of experience.
- OR PhD in Software Engineering, Computer Engineering, Information Technology, or related field with 3+ years of experience.
- Experience listed above should be in at least one of the following: DevOps, SRE, data engineering, or infrastructure engineering for production AI or distributed systems.
- LLM serving, retrieval infrastructure, and runtime control for non-deterministic systems.
- Graph-based or agent orchestration frameworks.
- Experience building RAG and knowledge-graph-backed systems for LLM applications in production.
- Python skills for orchestration, data pipeline development, and platform automation Preferred Qualifications • Experience designing multi-agent systems with clear autonomy boundaries and human-in-the-loop controls. • Track record in production evaluation frameworks for agent quality and safety. • Experience with observability and reliability for data and agent pipelines (metrics, logging, tracing, data quality monitoring). • Strong experience with Kubernetes, infrastructure as code, CI/CD, and production observability. • Deep experience with enterprise integration patterns and tools (e.g., RBAC, ABAC). • Ability to package data engineering practices into developer-friendly tooling and documentation. • Experience in regulated or enterprise environments requiring high trust and auditability.
- Join Intel and be part of a mission to lead the AI revolution.
- Innovate with us and shape the future of technology today.
Benefits
- We offer a total compensation package that ranks among the best in the industry.
- It consists of competitive pay, stock bonuses, and benefit programs which include health, retirement, and vacation.
- Find out more about the benefits of working at Intel .
- Annual Salary Range for jobs which could be performed in the US: $195,200.00-275,580.00 USD The range displayed on this job posting reflects the minimum and maximum target compensation for the position across all US locations.
- Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
- Your recruiter can share more about the specific compensation range for your preferred location during the hiring process.
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Specialisation
Open roles at Intel
767 positions
Job ID
/job/US-Arizona-Phoenix/AI-Software-Development-Engineer_JR0283985
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